Genetic Programming with Diverse Partner Selection for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{xu:2022:GECCOcomp2,
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author = "Meng Xu and Yi Mei and Fangfang Zhang and
Mengjie Zhang",
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title = "Genetic Programming with Diverse Partner Selection for
Dynamic Flexible Job Shop Scheduling",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference Companion",
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year = "2022",
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editor = "Heike Trautmann and Carola Doerr and
Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and
Marcus Gallagher and Yew-Soon Ong and
Abhishek Gupta and Anna V Kononova and Hao Wang and
Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and
Fabio Caraffini and Johann Dreo and Anne Auger and
Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Tea Tusar and Dimo Brockhoff and Tome Eftimov and
Pascal Kerschke and Boris Naujoks and Mike Preuss and
Vanessa Volz and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Mark Coletti and Catherine (Katie) Schuman and
Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and
Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and
Richard Allmendinger and Jussi Hakanen and
Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and
John McCall and Jaume Bacardit and
Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and
David Walker and Jamal Toutouh and UnaMay O'Reilly and
Penousal Machado and Joao Correia and Sergio Nesmachnow and
Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and
Francisco {Fernandez de Vega} and Giuseppe Paolo and
Alex Coninx and Antoine Cully and Adam Gaier and
Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and
Vesna Nowack and Aymeric Blot and Emily Winter and
William B. Langdon and Justyna Petke and
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and
Thomas Stuetzle and David Paetzel and
Alexander Wagner and Michael Heider and Nadarajen Veerapen and
Katherine Malan and Arnaud Liefooghe and Sebastien Verel and
Gabriela Ochoa and Mohammad Nabi Omidvar and
Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and
Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and
Jean-Baptiste Mouret and Stephane Doncieux and
Stefanos Nikolaidis and Julian Togelius and
Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and
Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and
Ofer Shir and Lee Spector and Alma Rahat and
Richard Everson and Jonathan Fieldsend and Handing Wang and
Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and
Michael Kommenda and William {La Cava} and
Gabriel Kronberger and Steven Gustafson",
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pages = "615--618",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, dynamic
flexible job shop scheduling, hyperheuristic, diverse
partner selection",
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isbn13 = "978-1-4503-9268-6/22/07",
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URL = "https://github.com/fangfang-zhang/fangfang-zhang.github.io/blob/master/files/2022-GP-Diverse-Partner-Selection.pdf",
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DOI = "doi:10.1145/3520304.3528920",
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size = "4 pages",
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abstract = "Dynamic flexible job shop scheduling (DFJSS) aims to
make decisions for machine assignment and operation
sequencing simultaneously to get an effective schedule
under dynamic environments. Genetic programming
hyper-heuristic (GPHH) has been successfully applied to
evolve scheduling heuristics for the DFJSS problem.
Parent selection plays an important role in GPHH for
generating high-quality offspring. Traditional GPHHs
select parents for crossover purely based on fitness
(e.g., tournament selection). This might be too greedy
to get good offspring and the selected parents might
have similar structures/behaviours. In this paper, a
GPHH method with a new diverse partner selection (DPS)
scheme is proposed, namely GPDPS, for DFJSS.
Specifically, we first define a new multi-case fitness
to characterise the behaviour of each scheduling
heuristic for DFJSS. Then, the newly proposed DPS
method selects a pair of complementary high-quality
parents for crossover to generate offspring. The
experimental results show that GPDPS significantly
outperforms the GPHH method on most of the DFJSS
scenarios, in terms of both test performance and
convergence speed.",
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notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Meng Xu
Yi Mei
Fangfang Zhang
Mengjie Zhang
Citations